A method considering and adjusting individual consistency and group consensus for group decision making with incomplete linguistic preference relations

Zhao, Meng; Ma, Xiaoyu; Wei, Dai-wei

doi:10.1016/j.asoc.2017.01.010

Cited by 40 publications

(19 citation statements)

References 64 publications

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“…These types of PRs need the DMs to offer numerical judgments. Because the decision‐making problems might be too complex to make quantitative judgments, linguistic variables (LVs) provided by Zadeh () are powerful that only need the DMs to offer qualitative preferences, such as “bad,” “fair,” and “good.” Using LVs, several types of linguistic fuzzy PRs (LFPRs) are proposed (Herrera, Herrera‐Viedma, & Verdegay, ; Xu, , ), and many group decision‐making methods with LFPRs are developed (Li, Dong, Herrera, Herrera‐Viedma, & Martinez, ; Liu et al ., 2019; Massanet, Riera, Torrens, & Herrera‐Viedma, ; Wu, Chiclana, & Herrera‐Viedma, ; Wang & Chen, ; Zhao, Ma, & Wei, ; Zhang & Guo, ).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Considering this situation, GDM is proposed. GDM with PRs has been deeply studied by researchers, where the group consensus is a hot researching topic (Li et al, ; Meng, An, & Chen, ; Massanet et al, ; Wu et al, ; Xu, Sun, & Wang, ; Zhao et al, ; Zhang & Guo, ). In consideration of GDM with HFLPRs, this section first introduces a new distance measure between HFLPRs.…”

Section: An Approach For Gdm With Hflprsmentioning

confidence: 99%

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Qualitative hesitant fuzzy group decision making: An additively consistent probability and consensus‐based perspective

et al. 2019

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Hesitant fuzzy linguistic preference relations (HFLPRs) can efficiently denote the hesitant qualitative judgments of decision makers. Consistency and consensus are two critical topics in group decision making (GDM) with preference relations. This paper uses the additively consistent concept for linguistic fuzzy preference relations (LFPRs) to give an additive consistency definition for HFLPRs. To judge the additive consistency of HFLPRs, 0‐1 mixed programming models (0‐1‐MPMs) are constructed. Meanwhile, additive‐consistency‐based 0‐1‐MPMs to ascertain missing values in incomplete HFLPRs are established. Following the consistent probability of LFPRs, an algorithm to calculate the linguistic priority weighting vector is presented. In consideration of the consensus of GDM, a consistency‐probability‐distance‐measure‐based consensus index is defined, and an interactive improving consensus method is provided. Finally, a method for GDM with HFLPRs is offered that can address incomplete and inconsistent cases. Meanwhile, numerical examples are offered, and comparative analysis is made.

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Section: Literature Reviewmentioning

confidence: 99%

Section: An Approach For Gdm With Hflprsmentioning

confidence: 99%

Qualitative hesitant fuzzy group decision making: An additively consistent probability and consensus‐based perspective

et al. 2019

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“…In the first group, a moderator or a manager assigns weights to experts directly (Cabrerizo et al, ; Lan, Sun, Chen, & Wang, ; Wei, ), the weights of the experts are determined for each of the criteria based on the expertise (Ölçer & Odabaşi, ), or experts assign weights to the other experts (Ramanathan & Ganesh, ). On the other hand, in indirect/objective methods, expert weights are allocated according to individual consistency (Zhao, Ma, & Wei, ), consensus measure (Dong, Zhang, & Herrera‐Viedma, ; Xu & Chen, ), or trust between experts (Wu, Chiclana, & Herrera‐Viedma, ). For instance, Dong et al () present a self‐management mechanism to generate decision makers' weights dynamically in the consensus process.…”

Section: Introductionmentioning

confidence: 99%

“…according to individual consistency (Zhao, Ma, & Wei, 2017), consensus measure (Dong, Zhang, & Herrera-Viedma, 2016b;Xu & Chen, 2007), or trust between experts (Wu, Chiclana, & Herrera-Viedma, 2015). For instance, Dong et al (2016b) present a self-management mechanism to generate decision makers' weights dynamically in the consensus process.…”

Section: Introductionmentioning

confidence: 99%

A cumulative belief degree approach for group decision‐making problems with heterogeneous information

Ervural

Kabak

2019

Expert Systems

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In some complex group decision‐making (GDM) problems, the information needing to be processed may be heterogeneous. This may involve consideration of objective and subjective criteria by experts who have their own particular set of criteria, their own preference format for assessing alternatives under these criteria, and who may themselves be assigned differing importance weights as experts. This paper presents a cumulative belief degree approach to cope with heterogeneous information in multiple attribute GDM problems. The proposed approach focuses to aggregate subjective expert assessments and objective criteria that are presented in various representation formats and scales. The methodology employs transformation formulae for several preference representation scales to belief structure, including 2‐tuple representation, classical fuzzy sets, hesitant fuzzy sets, and intuitionistic fuzzy sets. Aggregation formulae are proposed to combine expert criteria evaluations and find a collective preference. A consensus degree is calculated for measuring the agreement between the experts. An illustrative example is presented to clarify the steps of the methodology, and validity of the approach is assured through comparative analysis with the existing methods.

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“…The third aspect focuses on extending consensus models to different situations. Dynamic consensus model [27][28][29][30], consensus models considering social networks [2,19,22], soft consensus model [5], adaptive consensus model [20,30], and interactive consensus model [2,31] were proposed to make consensus models play better roles to meet different 2 Mathematical Problems in Engineering requirements and characteristics. It is obvious to find that a few of new ideas for solving the problems have been put forward in consensus models.…”

Section: Introductionmentioning

confidence: 99%

A Reliability-Based Consensus Model for Multiattribute Group Decision-Making with Analytically Evidential Reasoning Approach

Yang

Zhou

et al. 2018

Mathematical Problems in Engineering

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Expert reliability is the ability to make unmistakable evaluations on attributes for the performance of an alternative in multiattribute group decision making (MAGDM). It has a significant effect on the group consensus calculation and group decision-making; unfortunately the reliability has not been considered in the consensus-reaching model yet. This study focuses on providing a reliability-based consensus model for MAGDM with analytically evidential reasoning (analytical ER for short) approach. The basic probability assignment (BPA) function which can be discounted by expert reliability is introduced to describe the performance judgments of each expert, by combining which of the group judgments could be determined with analytical ER rule. Then the consensus degrees of three levels (attribute level, alternative level, and expert level) are defined by Jousselme distance to identify the experts who should revise their judgments and point out revised suggestions, based on which a decision-making method within interaction is proposed to determine the effective BPA functions of all experts and make final decision-making. Finally, a numerical case study is carried out to illustrate the effectiveness of the method.

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A method considering and adjusting individual consistency and group consensus for group decision making with incomplete linguistic preference relations

Cited by 40 publications

References 64 publications

Qualitative hesitant fuzzy group decision making: An additively consistent probability and consensus‐based perspective

Qualitative hesitant fuzzy group decision making: An additively consistent probability and consensus‐based perspective

A cumulative belief degree approach for group decision‐making problems with heterogeneous information

A Reliability-Based Consensus Model for Multiattribute Group Decision-Making with Analytically Evidential Reasoning Approach

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